Role Purpose We're looking for a Data Scientist to build and improve the models and rules that detect fraud, AML risk, and financial crime — from transaction monitoring to mule activity and account takeover. You'll own fraud analytics end to end, from data exploration to deployment and monitoring, working closely with fraud, compliance, and engineering teams.

 Key Responsibilities:

• Contribute to the development and deployment of fraud detection, AML, and financial crime prevention solutions.
• Build and improve models and rules that detect suspicious behavior, transaction fraud, mule activity, account takeover, sanctions risk, and abnormal customer patterns.
• Translate fraud, compliance, and business requirements into practical analytical solutions, detection scenarios, dashboards, and workflows.
• Own defined fraud analytics components end to end, from data exploration and feature engineering to testing, deployment, and monitoring.
• Work with structured financial, customer, device, transaction, and case-management data to identify fraud patterns and risk indicators.
• Conduct experimentation and optimization of fraud detection models, risk scoring logic, and scenario thresholds.
• Support model deployment and monitoring in collaboration with engineering teams, ensuring fraud models remain accurate, explainable, and operationally useful.
• Assist in tuning fraud scenarios, reducing false positives, improving detection rates, and measuring model effectiveness.
• Interact with SME clients under guidance to understand fraud use cases, operational pain points, and regulatory expectations.
• Stay updated with fraud trends, AML typologies, regulatory requirements, and industry best practices.